EnLoc: Energy-Efficient Localization
for Mobile Phones
Written By, Ionut Constandache (Duke),
Shravan Gaonkar (UIUC),
Matt Sayler (Duke),
Romit Roy Choudhary (Duke),
London Cox (Duke)
OUTLINE OF PAPER
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Aim
Energy measurement
Framework design
System design
Performance evaluation
Limitations and Future work
Why EnLoc?
• Number of location based applications on mobile
phones is growing.
• Most of them use GPS for its location
accuracy(10m).
• GPS drains off lots of energy of mobile phone
reducing its battery life to less than 9 hours.
• Energy efficient localization framework: EnLoc
• It provides optimal localization accuracy for a
given energy budget.
Observations for energy-accuracy profile
1. Energy budget for localization is assumed to be
25% of battery capacity.
2. For some application continuous GPS localization
is necessary.
3. WiFi and GSM cannot be obvious replacements
to GPS because of their higher localization error.
Energy Measurements
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Power consumption in Nokia N95 is measured by
a software monitor.
Reference work: Project Place Lab proposed
using WiFi and GSM sensors for localization.
Energy consumption on Nokia N95 phones is
measured for each localization sensor.
Power consumption on Nokia N95
Type of
Sensor
Battery Life
( in hours)
Location
Accuracies
(in meters)
GPS
9
10
WiFi
40
40
GSM
60
400
• Power consumption was
translated to net battery
life
• Energy-accuracy tradeoff
can be seen from this table.
Framework Design
1. Average Location Error (ALE):
Here, GPS is assumed to be the ground truth.
2. The basic aim: for a given energy budget,
determine a schedule with which the location
sensors should be triggered such that ALE is
minimized.
Schedule: set of time instants, {t1, t2, t3,..} and
corresponding sensors {s1,s2,s3,..}, where si ɛ
{GPS, WiFi, GSM}
This is done by Dynamic Programming (DP)
Trace Opt
GPS
Opt
WiFi
Opt
GSM
Opt
Comb
1
164.999
78.52
352.909
78.52
2
105.35
75.16
327.116
58.66
3
125.848
62.134 370.621
62.134
3. Optimal Localization
error:
DP was executed on a
trace obtained by a
custom simulator that
integrated the traces with
the wireless map.
energy budget: 25%
duration of operation: 24
hours
4. Prediction opportunities:
A. Simple Linear Predictor
B. Human Mobility Patterns
C. Deviations
System Design
1. Habitual Mobility:
individuals exhibit habitual space-time
movements, with reasonably small variation.
GPS mobility traces of several people are plotted
on a google map to form a Logic Mobility Tree
(LMT).
Vertices of this tree are uncertainty points.
key idea: scheduling location readings right after
the uncertainty points on LMT.
Example
• Lets assume its 8 am and
phone is at home
• Consider two paths of the
tree
P1 = home=>coffee
P2 = home=>walmart
• Ni: # of location readings
N1 = 4+6 = 10
N2 = 4+8+3 = 15
• M = max(Ni) = 15
• F = eH/M = 4/15
Example
• Amount of energy for detecting its departure from
home = F * Bremaining = 0.26 * 10 = 2.6
• Thus, approximately 2 GPS readings are available
• Heuristic randomly chooses 2 time points out of 4
and samples the phone’s location.
• Once phone is found on one of its paths going out
of home, heuristic predicts phones location based
on habitual velocity on that edge.
• At next uncertainty point, F is recomputed.
Deviation from Habits
• This is coped up with exploiting mobility of large
populations as a potential indicator of the
individual’s mobility.
• Deviation mode is switched on when scheduled
location reading finds out that the phone is in an
unexpected location.
• In this mode, population activity map is used to
predict the phone’s movement.
Performance Evaluation
• EnLoc is evaluated by
using traces collected on
UIUC campus.
1. Evaluation of deviant
paths:
mobility of phone is
predicted based on
maximum probability at
the intersection.
Performance Evaluation
2. Habitual Mobility
person’s mobility profile is
utilized for prediction.
a student’s mobility profile
was derived from 30 days
of traces.
LMT is manually
generated and mobility
profile heuristic was
executed for each day.
Limitations and Future Work
1. When the location of a phone is tracked while
moving along a predicted path, varying speeds or
pauses cause imprecise prediction. These errors
are not accounted in evaluation results.
2. GPS reading scheduled after an intersection can
be eliminated by using compass.
3. EnLoc does not proactively identify deviations
from habitual paths.
4. Generating probability maps for places unlike
university campuses may be difficult.
Critique
1. Authors have not elaborated Dynamic
Programming due to space constraints.
2. Lot of overhead in maintaining an individual
habitual map.
3. It is not mention how much exact amount of
energy is conserved by using this framework.
Questions???